Find Academic Supervisors

• Wireless Communications
• Signal Processing
• Physical Layer Security
• +3 more

Georges Kaddoum is a Professor at École de technologie supérieure (ÉTS) , specializing in Electrical Engineering. He holds the Canada Research Chair in Unlocking the Power of IoT 6G-Networks and the LACIME – Communications and Microelectronic Integration Laboratory affiliation. His work bridges wireless communications, IoT, and machine learning. Research Interests include wireless communication systems, physical layer security, machine learning for networking, and 6G technologies. He focuses on optimizing network performance in challenging environments (impulsive noise, underwater, non-terrestrial networks) and developing AI-driven solutions for jamming mitigation, resource allocation, and secure IoT frameworks. Recent Publications highlight 6G-enabled vehicular networks, quantum-safe blockchain integration, federated learning for transportation systems, and deep learning-based receivers for chaotic communication systems. His work emphasizes semantic communication, interference management, and digital twin applications. Awards IEEE TCSC Award for Excellence in Scalable Computing (2022) Prix d’excellence de la relève (Université du Québec, 2018) Prix d’excellence en recherche (ÉTS, 2018) Multiple IEEE Exemplary Reviewer and Best Paper Awards (2014–2022) Supervision includes 15+ PhD/Master’s students working on topics like index modulation, physical layer security, UAV communications, and intelligent resource management. Research Units Ultra Research Chair on Intelligent Tactical Wireless Networks LACIME Laboratory Canada Research Chair in IoT 6G-Networks

Vasant DharView profile

Professor

• Artificial Intelligence
• Data Science
• Machine Learning
• +5 more

Vasant Dhar is the Robert A Miller Professor of Business and Professor of Data Science at the Leonard N. Stern School of Business at New York University. He serves as Director of Industry Relations and specializes in Technology, Operations, and Statistics. Joining Stern in 1983, Professor Dhar has established himself as a leading expert in artificial intelligence, data science, and financial technology. Professor Dhar's educational background includes: Ph.D. in Artificial Intelligence from the University of Pittsburgh (1984) M.Phil. from the University of Pittsburgh (1982) B.Tech. in Chemical Engineering from the Indian Institute of Technology, Delhi (1978) His research focuses on how risk influences our trust in AI systems, demonstrating the existence of an "automation frontier" that expresses a tradeoff between how often machines will be wrong and the consequences of their errors. Professor Dhar examines how innovations such as Artificial Intelligence impact our lives, and how we can create technology and policy for a better future in a world of increasingly intelligent machines. His work spans financial applications of AI, where he was among the first to bring machine learning to Wall Street in the 1990s, founding the machine-learning-based hedge fund SCT Capital Management. Professor Dhar's recent publications reveal a strong focus on the practical applications and societal implications of AI. His work addresses critical issues including AI reliability in financial document analysis, the governance of AI companies, ethical considerations in biometric payments, and the evolving relationship between humans and increasingly intelligent machines. His research demonstrates how AI is transforming various sectors while raising important questions about trust, accountability, and the future of work. Among his notable recognitions is the Robert A Miller Professorship, an endowed chair position at NYU Stern. His research has been funded by grants from industry and government agencies such as the National Science Foundation. Professor Dhar teaches courses on Systematic Investing, Data Science, Prediction, and Tech Innovation. He has written over 100 research articles and is the host of the "Brave New World" podcast, which explores how technology and virtualization in the post-COVID era is transforming humanity. He publishes fortnightly at vasantdhar.substack.com and is a frequent speaker in academic and industrial forums.

• Autonomous Driving
• Digital Twin
• Human-Machine Interaction
• +3 more

Ziran Wang is an Assistant Professor in the Department of Civil Engineering at Purdue University's College of Engineering, appointed as new faculty in 2022. His research bridges digital twin technologies, autonomous driving systems, and human-machine interaction to advance intelligent transportation solutions. Ph.D. in Mechanical Engineering, University of California, Riverside Prior role: Principal Researcher at Toyota North America His work focuses on creating personalized autonomous driving experiences through machine learning, emphasizing safety and efficiency in real-world applications. Key areas include multimodal large language model integration, federated learning for privacy-preserving data sharing, and cooperative perception frameworks. He develops novel approaches for digital twin-based traffic simulation, medical emergency detection in vehicles, and human behavior modeling in complex urban environments. Analysis of his 2024-2025 publications reveals a dominant trend toward generative AI applications in autonomous driving, particularly for perception-prediction-planning integration and real-world validation. His research increasingly incorporates digital twins for safety-critical testing and explores medical applications through in-vehicle health monitoring systems. Dr. Wang advises graduate students including Wenhui Huang and leads the Purdue Digital Twin Lab, which develops advanced simulation and testing platforms for autonomous systems. His lab maintains strong industry partnerships with Toyota for real-world deployment and validation of research成果.

Jinjin GuView profile

Assistant Professor

• Computer Science
• Artificial Intelligence
• Computer Vision
• +7 more

Jinjin Gu is a tenure-track Assistant Professor at Sofia University "St. Kliment Ohridski" 's INSAIT (Institute for Computer Science, Artificial Intelligence, and Technology), leading research on visual cognition and intelligence. Her work spans visual perception, processing, generation, and reasoning. Education: Ph.D. in Electrical and Computer Engineering (2024), University of Sydney B.Sc. in Computer Science and Engineering (2020), Chinese University of Hong Kong, Shenzhen Her research focuses on visual cognition , including agentic systems , diffusion models , GAN architectures , model interpretability , super-resolution , and multimodal vision-language systems . She has developed novel paradigms like HYPIR for diffusion-quality restoration at GAN speeds. Recent publications highlight advancements in image/video restoration , generative modeling , and visual reasoning . Her work addresses critical challenges in model generalization , causal interpretation , and real-world application robustness . Scientific Awards: Stanford University's World's Top 2% Scientists (2024) Yunfan Award at World Artificial Intelligence Conference (WAIC) (2023) She has advised students contributing to TPAMI, CVPR, and ICLR publications, and serves as Area Chair for ICLR 2026, NeurIPS 2025, and ICML 2025.

Erik VelldalView profile

Professor

• Natural Language Processing
• Machine Learning
• Sentiment Analysis
• +5 more

Erik Velldal is a Professor in the Language Technology Group (LTG) at the Section for Machine Learning , Department of Informatics, University of Oslo . With over 25 years of experience in machine learning and natural language processing (NLP), he leads the SANT project focused on sentiment analysis and contributes to major research initiatives including MediaFutures , NorwAI , and Integreat (Norwegian Center for AI Research). His work bridges linguistic theory and computational methods, emphasizing semantic modeling and uncertainty detection. Research interests include sentiment analysis , language modeling , event extraction , and machine learning applications to NLP. Recent publications address cross-domain sentiment classification , generative event analysis , and multilingual model adaptation . He co-developed the Norwegian Review Corpus (NoReC) and Norwegian Anaphora Resolution Corpus (NARC) , foundational resources for Norwegian NLP. His projects often involve collaboration with international institutions, reflected in publications at venues like ACL, COLING, and EMNLP. Current efforts focus on entity-level sentiment analysis , diagnostic datasets for Norwegian , and evaluating compositional generalization in language models. No public record of scientific awards or part-time appointments exists.

Xiaofeng QianView profile

Associate Professor

• Materials Theory
• Quantum Materials
• 2D Materials
• +7 more

Dr. Xiaofeng Qian is an Associate Professor in the Department of Materials Science & Engineering at Texas A&M University, with joint appointments in Physics and Astronomy, and Electrical & Computer Engineering. His research focuses on materials theory , quantum materials design , and high-throughput computational discovery , particularly for 2D materials and energy applications . Educational Background: Ph.D., Nuclear Science and Engineering, Massachusetts Institute of Technology (2008) B.S., Engineering Physics, Tsinghua University (2001) Research spans first-principles electronic structure methods , nonlinear optical responses , and multiscale modeling of electronic, thermal, and ionic transport. Key areas include quantum spin Hall effect , ferroelectric switching , and machine learning for materials prediction . Notable Awards: Dean of Engineering Excellence Award (2024) Engineering Genesis Multidisciplinary Award (2024) AZZ Faculty Fellow (2021) NSF CAREER Award (2018) Manson Benedict Fellowship (2006) Actively recruiting PhD, MS, and UG researchers with backgrounds in physics, materials science, or computational methods. Collaborates extensively on hybrid AI-materials projects and topological device concepts .

Pascal MettesView profile

Assistant Professor

• Hyperbolic Deep Learning
• Computer Vision
• Vision-Language Models
• +3 more

Pascal Mettes is a tenured Assistant Professor at the University of Amsterdam within the Informatics Institute, specializing in Artificial Intelligence. He leads groundbreaking research in hyperbolic deep learning, a field he has significantly advanced through theoretical developments and practical applications in computer vision and multimodal learning. His research focuses on three primary domains: hyperbolic vision-language models that address the hierarchical nature of language-vision relationships; hierarchical deep learning using hyperbolic embeddings that naturally accommodate exponential growth patterns; and robust deep learning in hyperbolic space that improves out-of-distribution detection and network resilience. Mettes has established himself as a leading figure in this emerging field through numerous publications at top-tier conferences including CVPR, ICCV, ICML, NeurIPS, and ICLR. His recent work demonstrates how hyperbolic geometry provides natural solutions to fundamental limitations in modern deep learning, particularly regarding hierarchical data structures that cannot be adequately represented in Euclidean space. The publication trends show increasing impact and recognition in the computer vision and machine learning communities, with multiple papers receiving oral presentations and best paper nominations. Best paper nomination ESWC25 for 'Designing Hierarchies for Optimal Hyperbolic Embedding' Finalist MM 2023 Best Open-Source Software Competition (for HypLL) Multiple reviewer awards across major conferences including CVPR, ICLR, ECCV, ICML, and NeurIPS MM 2016 Best Doctoral Student Award TRECVID 2015 Winner Multimedia Event Detection Benchmark Mettes actively mentors eight PhD students working on hyperbolic learning and related topics, while also securing significant research funding including ELLIs PhD Award, NWO ClickNL, Google Perception Academic Funding, and Data Science Centre PhD Grants. He serves in prominent academic roles as Program Chair for International Conference on Multimedia Retrieval 2026 and has organized multiple workshops on hyperbolic learning at major conferences. His leadership in establishing hyperbolic deep learning as a recognized research direction is evident through his survey paper in IJCV 2024 and the development of the HypLL library for hyperbolic learning.

Xiaocheng ShangView profile

Associate Professor

• Mathematical Optimization
• Data Science
• Numerical Analysis
• +3 more

Xiaocheng Shang is an Associate Professor in Mathematical Optimisation and Data Science at the University of Birmingham's School of Mathematics. His research focuses on numerical methods for stochastic differential equations, with applications in computational mathematics, statistics, physics, and data science. He is affiliated with the Optimisation and Numerical Analysis Group, Statistics and Data Science Group, and the Institute for Data and AI. Shang holds a PhD in Applied and Computational Mathematics from the University of Edinburgh (2016) and completed postdocs at the University of Edinburgh, Brown University, and ETH Zurich before joining Birmingham in 2019. His academic achievements include fellowships from The Alan Turing Institute, the LMS Emmy Noether Fellowship, and the EUniWell Leadership Fellowship. He has secured funding from EPSRC, the Royal Society, and the Isaac Newton Institute. Shang is actively involved in supervising PhD students and co-organizing research initiatives such as the Data Science and Computational Statistics Seminar. Research interests include structure-preserving integrators, Bayesian sampling techniques, and machine learning applications in dynamical systems. His work bridges numerical analysis, probability theory, and multiscale modeling in materials science. Recent projects involve neural networks for complex dynamical systems and numerical algorithms for deterministic/stochastic systems.

Mihaela van der SchaarView profile

Professor

• Machine Learning
• Artificial Intelligence
• Healthcare
• +8 more

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence, and Medicine at the University of Cambridge, leading the van der Schaar Lab. She holds dual affiliations with the Department of Applied Mathematics and Theoretical Physics (DAMTP) and the Centre for Mathematical Imaging in Healthcare. Her research focuses on healthcare AI, machine learning, and operations research. She has authored over 250 journal articles and 275 conference papers, with notable contributions to synthetic data for privacy, causal inference, and clinical decision-making. Her work has led to 35 U.S. patents, including foundational innovations in streaming video compression (MPEG-4 standards). Awards include the Oon Prize (2018), IEEE Fellow (2009), and recognition as the UK's most-cited female AI researcher (2019). Leadership roles include Director of the Cambridge Centre for AI in Medicine and Co-Director of the European Laboratory for Learning and Intelligent Systems. She has mentored global academic leaders and pioneered initiatives like the Inspiration Exchange for early-career researchers. Key projects include predictive models for hospital resource allocation during pandemics and AI tools for personalized medicine. Publications span machine learning theory, healthcare applications, and interdisciplinary fields like network science. Her lab's impact includes tools like AutoPrognosis (automated ML for clinical prediction) and SynthCity (synthetic healthcare data generation).

Duminda WijesekeraView profile

Professor

• Cybersecurity
• Cyber-Physical Systems
• Digital Forensics
• +5 more

Duminda Wijesekera serves as Professor in the Department of Cyber Security Engineering and Department of Computer Science at George Mason University, where he was inaugural chairman of the Cyber Security Engineering Department until December 2022. He concurrently held the position of visiting research scientist at the National Institute of Standards and Technology (NIST) from 2007-2022 and maintains status as a fellow at the Potomac Institute of Policy Studies. He leads the Mason Innovation Laboratory at Mason Square, driving translational research in cyber-physical security. His educational foundation includes: PhD in Computer Science, University of Minnesota (1997) PhD in Mathematical Logic, Cornell University (1990) BSc in Mathematics, University of Colombo Professor Wijesekera's research centers on cyber-physical system security , with pioneering work in Intelligent Transportation Systems spanning trains, aircraft, and connected vehicles. His digital forensics innovations establish frameworks for evidence-based scenario reconstruction and error management, while his formal methods research provides mathematical guarantees for safety-critical systems. Current projects address Next G-based edge services, digital twin vulnerability detection, and healthcare security architectures, consistently bridging theoretical rigor with real-world infrastructure protection. Analysis of his 2022-2025 publications reveals intense focus on autonomous vehicle security (38% of recent output), including traffic signal control optimization, ramming attack countermeasures, and CARLA-based scenario validation. Digital forensics using AI (20%) and secure manufacturing/edge computing (27%) constitute other major thrusts, demonstrating how formal verification and machine learning converge to solve complex cyber-physical security challenges across transportation, energy, and healthcare domains. His scientific recognition includes: CCI Impact Award (2022) for groundbreaking cyber-physical security contributions Fellowship at the Potomac Institute of Policy Studies for cybersecurity policy leadership Professor Wijesekera has secured substantial research funding through: NIST grants for health record security frameworks (2014-2015) US Department of Transportation projects on wireless frequency mapping for high-speed rail (2013-2014) Cyber Security Research Alliance funding for trust architectures in cyber-physical systems (2014) Commonwealth Cyber Initiative awards for autonomous vehicle security and energy-efficient manufacturing His industry partnerships with Honeywell and NIST ensure practical impact of theoretical research. The Mason Innovation Laboratory under his direction serves as an interdisciplinary hub for cyber-physical security, integrating researchers from computer science, electrical engineering, and policy studies to develop deployable solutions for transportation networks, power grids, and critical infrastructure protection.

Inga Kristina TrauthigView profile

Research Professor

• Terrorism and Extremism
• Disinformation & Media
• Emerging Technologies
• +3 more

Inga Kristina Trauthig is a dual-affiliation academic serving as a Research Professor at Florida International University’s Jack D. Gordon Institute for Public Policy and a Visiting Fellow at King’s College London’s Institute of Middle Eastern Studies (IMES). She holds a PhD in War Studies from King’s College London, focusing on Libya, and an MLitt in Middle East Security from the University of St Andrews. Her research spans terrorism, disinformation, emerging technologies, and Middle Eastern political dynamics, with particular expertise in Salafism and security sector analysis. She has authored over 80 peer-reviewed publications and policy-focused articles, including work in Conflict, Security & Development and Political Research Quarterly . Her consulting work addresses causes of war and insecurity for UN bodies, governments, NGOs, and private firms. She has provided oral evidence to the UK Parliament and appeared on platforms like BBC’s Digital Human and Lawfare Blog . Trauthig convenes King’s MENA Research Group within IMES and has held student representation roles across faculty levels. Her research highlights include analyzing generative AI’s political risks, disinformation strategies in MENA, and the role of messaging apps in authoritarian governance.

Samuel ChengView profile

Associate Professor

• Information Theory
• Signal and Image Processing
• Remote Sensing
• +4 more

Dr. Samuel Cheng is an Associate Professor at the Gallogly College of Engineering , University of Oklahoma , specializing in Electrical and Computer Engineering . He holds a Ph.D. in Electrical Engineering from Texas A&M University (2004), preceded by M.S. and M.Phil. degrees from the University of Hawaii and Hong Kong University of Science and Technology. Education: B.S. (University of Hong Kong, 1995), M.Phil. (HKUST, 1997), M.S. (University of Hawaii, 2000), Ph.D. (Texas A&M, 2004) Professional Experience: Senior Research Engineer at Advanced Digital Imaging Research (2004-2005), prior internships at Microsoft Asia and Panasonic Technologies His research focuses on Information Theory , Signal and Image Processing , and Pattern Recognition , with applications in remote sensing, urbanization analysis, and disaster monitoring. His publications span topics including urban impervious surface mapping , nighttime light analysis , and machine learning for environmental data . His work often integrates multi-source datasets (e.g., Landsat, LiDAR, social media) for spatiotemporal modeling. Technical Expertise: Spectral unmixing, machine learning, thermal remote sensing, GIS integration Key Applications: Power outage detection, vegetation-crime correlation, PM2.5 estimation, smart meter data fusion Dr. Cheng holds three US patents in digital watermarking and is affiliated with IEEE, Sigma Xi, and AAAS. His recent articles demonstrate a trend toward leveraging AI for remote sensing challenges and analyzing urbanization impacts on ecosystems.

• Narrative Theory
• Fictionality Studies
• Literary Cognition
• +4 more

Richard Walsh is Professor in the Department of English and Related Literature at the University of York, where he leads the Interdisciplinary Centre for Narrative Studies and founded the British and Irish Association for Narrative Studies. His research bridges literary theory, cognitive science, and complex systems, examining narrative as a fundamental mode of human cognition. His research focuses on three interconnected domains: The rhetorical theory of fictionality and its cultural manifestations Interdisciplinary approaches to narrative cognition and sensemaking Complexity science applications to narrative structures across media This work extends to innovative fiction, digital narratives, and cross-cultural storytelling. Analysis of Walsh's recent publications reveals strong emphasis on narrative cognition, interdisciplinary methodologies, and emerging topics in AI storytelling. Thematic clusters include: Foundational studies in fictionality and rhetorical frameworks Complexity theory applications to narrative dynamics Interdisciplinary intersections with cognitive science and digital media His scholarly recognition includes the distinguished Institute of Advanced Study Research Fellowship at Durham University (2016) Walsh directs multiple research groups including the Narrative and Complex Systems Group (NarCS) and supervises doctoral projects on narrative theory. He leads collaborative initiatives like the RIDERS project on emergent narrative and the Threshold Worlds project on dream cognition.

• Machine Learning
• Artificial Intelligence
• Explainability of AI
• +3 more

Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University and serves as Director of the Stanford AI Lab (SAIL) and Senior Fellow at the Institute for Human-Centered AI (HAI). He holds dual roles as Chief Scientist at Visual Layer and Virtue AI. His research focuses on machine learning methods, explainability, fairness, and ethics of AI, alongside systems for scalable AI deployment. Education details are not explicitly provided, but his work spans foundational contributions to machine learning systems (e.g., XGBoost) and explainable AI frameworks like Anchors and LIME. He emphasizes ethical AI through projects like CheckList for model testing and Model Equality Testing for API transparency. His scientific contributions include advancing optimization techniques (AdaScale SGD, TVM compiler) and ethical benchmarks for generative AI. He has been recognized as a Member of the National Academy of Engineering for his transformative impact on AI systems and their societal applications. Guestrin leads interdisciplinary initiatives at SAIL and HAI, fostering collaboration between technical innovation and human-centered design. His work bridges theory and practice, addressing challenges in healthcare (diabetes management systems) and AI security.

Matei CiocarlieView profile

Associate Professor

• Robotic Manipulation
• Assistive Robotics
• Human-Robot Interaction
• +5 more

Matei Ciocarlie is an Associate Professor of Mechanical Engineering at Columbia University, focusing on robotics research spanning hardware design, control systems, and human-robot interaction. His work emphasizes robots operating in dynamic, unstructured environments, with applications in manufacturing, logistics, and healthcare. He holds affiliations with Columbia's School of Engineering and Applied Science and the Robotics Department. Education: Ph.D. in Mechanical Engineering from Columbia University (2010), with a doctoral dissertation on dexterous robotic grasping awarded the 2010 Robotdalen Scientific Award. Research Interests: Robotic hand design/control, human-in-the-loop manipulation, assistive robotics, tactile sensing, and rehabilitation technologies. His lab develops systems like wearable robotic orthoses for stroke patients and teleoperated robots with autonomous decision-making capabilities. Awards: IEEE Early Career Award (2015), ONR Young Investigator Award (2016), NSF CAREER Award (2016), Sloan Fellowship (2017). Grants & Collaborations: Led projects at Willow Garage and Google, contributing to ROS development. Current work includes NSF-funded tactile sensing research and NIH-sponsored assistive robotics initiatives. Labs: Active projects in Columbia's Robotics Lab and collaboration with medical institutions for clinical trials of robotic orthoses.